Sustainable Climatic Metrics Determination with Ensemble Predictive Analytics

Title

Sustainable Climatic Metrics Determination with Ensemble Predictive Analytics

Creator

Pattanaik A.; Sharma V.; Kunj K.; Mishra S.; Iwendi C.; Osamor J.

Description

Sustainable features are dependent on vital climatic elements that has a prominent impact on the retention of sustainability provided its metrics are in desired domain. Regression analysis and ensemble learning models are some of the predictive analytics methods which were used to detect the association of every feature on sustainable criteria. Weather samples from Delhi during 1970-2020 is used in the research which considers features like humidity, pollutant level, temperature etc which are gathered from several authenticated sites like pollution management unit of India. After analyzing several elements affecting weather endurability, it is noticed that pollutant level and temperature exhibit the highest significance recording 30% and 44% respectively. Also the R-square metric of 86% and 82% was observed with implementation of analytics models. The major conclusion recorded that random forest outperformed regression model and it established the importance of predictive analytics in predicting sustainability results. The research validated the relevance of climatic tracking for regulating sustainability. 2023 IEEE.

Source

2023 4th International Conference on Computation, Automation and Knowledge Management, ICCAKM 2023

Date

2023-01-01

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

Climate change; Linear regression; Machine learning; Random forest; Sustainability

Coverage

Pattanaik A., Kalinga Institute of Industrial Technology, (Deemed to Be University), India; Sharma V., Christ (Deemed to Be University), Department of Computational Sciences, Delhi NCR, India; Kunj K., Kalinga Institute of Industrial Technology, (Deemed to Be University), India; Mishra S., Kalinga Institute of Industrial Technology, (Deemed to Be University), India; Iwendi C., School of Creative Technologies, University of Bolton, United Kingdom; Osamor J., Glasgow Caledonian University, Department of Cybersecurity and Networks, Glasgow, G4 0BA, United Kingdom

Rights

All Open Access; Green Open Access

Relation

ISBN: 979-835039324-8

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/ICCAKM58659.2023.10449578

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187553292&doi=10.1109%2fICCAKM58659.2023.10449578&partnerID=40&md5=26a71c24c5e842aa6df092d1ec7e5f76